*
* Example IBMNYLN.DAT from pp 548-556
*
open data ibmnyln.dat
data(format=free,org=columns) 1 242 date london ny
*
graph(footer="Figure 13-12 IBM Stock Price, New York and London",key=attached,$
 vlabel="NY in $, London in pounds") 2
# london
# ny
*
@crosscorr(report) london ny
*
@bjident(diffs=1) london
*
* Analysis with London price as possible leading variable
*
boxjenk(diffs=1,const) london
@regcorrs
boxjenk(diffs=1) london
@regcorrs
boxjenk(diffs=1,ma=1) london
set londone = %resids
*
* This uses the last ARIMA model to filter the NY variable. The output of this is
* in the %resids series.
*
boxjenk(diffs=1,ma=1,method=eval,initial=%beta) ny
set newyorke = %resids
@crosscorr(report) londone newyorke
*
* Analysis with New York price as possible leading variable
*
boxjenk(diffs=1) ny
@regcorrs
*
diff ny / newyorke
diff london / londone
*
@crosscorr(report) newyorke londone
*
* Do the base transfer function model
*
boxjenk(const,inputs=1) londone
# newyorke 0 0 1
@regcorrs
*
* Add in the MA(1) noise term and drop the constant
*
boxjenk(ma=1,inputs=1) londone
# newyorke 0 0 1
@regcorrs
*
* Check the cross correlations
*
@crosscorr newyorke %resids
*
* Rerun regression in undifferenced variables
*
boxjenk(diffs=1,ma=1,inputs=1,applydiffs) london
# ny 0 0 1
prj fitted
print(picture="*.###") 100 150 ny london fitted %resids